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Nevena Lazic

Nevena Lazic

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    Robotic Table Tennis: A Case Study into a High Speed Learning System
    Jon Abelian
    Saminda Abeyruwan
    Michael Ahn
    Justin Boyd
    Erwin Johan Coumans
    Omar Escareno
    Wenbo Gao
    Navdeep Jaitly
    Juhana Kangaspunta
    Satoshi Kataoka
    Gus Kouretas
    Yuheng Kuang
    Corey Lynch
    Thinh Nguyen
    Ken Oslund
    Barney J. Reed
    Anish Shankar
    Avi Singh
    Grace Vesom
    Peng Xu
    Robotics: Science and Systems (2023)
    Preview abstract We present a deep-dive into a learning robotic system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the ability to precisely return the ball to desired targets. This system puts together a highly optimized and novel perception subsystem, a high-speed low-latency robot controller, a simulation paradigm that can prevent damage in the real world and also train policies for zero-shot transfer, and automated real world environment resets that enable autonomous training and evaluation on physical robots. We complement a complete system description including numerous design decisions that are typically not widely disseminated, with a collection of ablation studies that clarify the importance of mitigating various sources of latency, accounting for training and deployment distribution shifts, robustness of the perception system, and sensitivity to policy hyper-parameters and choice of action space. A video demonstrating the components of our system and details of experimental results is included in the supplementary material. View details
    A Maximum-entropy Approach to Off-policy Evaluation in Average-reward MDPs
    Dong Yin
    Mehrdad Farajtabar
    Nir Levine
    Dilan Gorur
    Chris Harris
    Neural Information Processing Systems (NeurIPS) (2020)
    Preview abstract This work focuses on off-policy evaluation (OPE) with function approximation in infinite-horizon undiscounted Markov decision processes (MDPs). For MDPs that are ergodic and linear (i.e. where rewards and dynamics are linear in some known features), we provide the first finite-sample OPE error bound, extending existing results beyond the episodic and discounted cases. In a more general setting, when the feature dynamics are approximately linear and for arbitrary rewards, we propose a new approach for estimating stationary distributions with function approximation. We formulate this problem as finding the maximum-entropy distribution subject to matching feature expectations under empirical dynamics. We show that this results in an exponential-family distribution whose sufficient statistics are the features, paralleling maximum-entropy approaches in supervised learning. We demonstrate the effectiveness of the proposed OPE approaches in multiple environments. View details
    Preview abstract We propose a model-free algorithm for learning efficient policies capable of returning table tennis balls by controlling robot joints at a rate of 100Hz. We demonstrate that evolutionary search (ES) methods acting on CNN-based policy architectures for non-visual inputs and convolving across time learn compact controllers leading to smooth motions. Furthermore, we show that with appropriately tuned curriculum learning on the task and rewards, policies are capable of developing multi-modal styles, specifically forehand and backhand stroke, whilst achieving 80\% return rate on a wide range of ball throws. We observe that multi-modality does not require any architectural priors, such as multi-head architectures or hierarchical policies. View details
    Politex: Regret Bounds for Policy Iteration using Expert Prediction
    Yasin Abbasi-Yadkori
    Peter Bartlett
    Kush Bhatia
    Gellért Weisz
    ICML (2019)
    Preview abstract We present POLITEX (POLicy ITeration with EXpert advice), a variant of policy iteration where each policy is a Boltzmann distribution over the sum of action-value function estimates of the previous policies, and analyze its regret in continuing RL problems. We assume that the value function error after running a policy for m time steps scales as E(m) = E0 + O((d/m)^{1/2}), where E0 is the worst-case approximation error and d is the number of features in a compressed representation of the state-action space. We establish that this condition is satisfied by the LSPE algorithm under certain assumptions on the MDP and policies. Under the error assumption, we show that the regret of POLITEX in uniformly mixing MDPs scales as O(d^{1/2}T^{3/4} + E0T), where O(.) hides logarithmic terms and problem-dependent constants. Thus, we provide the first regret bound for a fully practical model-free method which only scales in the number of features, and not in the size of the underlying MDP. Experiments on a queuing problem confirm that POLITEX is competitive with some of its alternatives, while preliminary results on Ms Pacman (one of the standard Atari benchmark problems) confirm the viability of POLITEX beyond linear function approximation. View details
    Preview abstract Model-free approaches for reinforcement learning (RL) and continuous control find policies based only on past states and rewards, without fitting a model of the system dynamics. They are appealing as they are general purpose and easy to implement; however, they also come with fewer theoretical guarantees than model-based RL. In this work, we present a new model-free algorithm for controlling linear quadratic (LQ) systems, and show that its regret scales as O(T^(ξ+2/3)). The algorithm is based on a reduction of control of Markov decision processes to an expert prediction problem. In practice, it corresponds to a variant of policy iteration with forced exploration, where the policy in each phase is greedy with respect to the average of all previous value functions. This is the first model-free algorithm for adaptive control of LQ systems that provably achieves sublinear regret and has a polynomial computation cost. Empirically, our algorithm dramatically outperforms standard policy iteration, but performs worse than a model-based approach. View details
    Data Center Cooling using Model-predictive Control
    Tyler Lu
    MK Ryu
    Eehern Jay Wong
    Binz Roy
    Greg Imwalle
    Proceedings of the Thirty-second Conference on Neural Information Processing Systems (NeurIPS-18), Montreal, QC (2018), pp. 3818-3827
    Preview abstract Despite the impressive advances in reinforcement learning (RL) algorithms, their deployment to real-world physical systems is often complicated by unexpected events and the potential for expensive failures. In this paper we describe an application of RL “in the wild” to the task of regulating temperatures and airflow inside a large-scale data center (DC). Adopting a data-driven model-based approach, we demonstrate that an RL agent is able to effectively and safely regulate conditions inside a server floor in just a few hours, while improving operational efficiency relative to existing controllers. View details
    Preview abstract We study the problem of controlling linear time-invariant systems with known noisy dynamics and adversarially chosen quadratic losses. We present the first efficient online learning algorithms in this setting that guarantee O(√T) regret under mild assumptions, where T is the time horizon. Our algorithms rely on a novel SDP relaxation for the steady-state distribution of the system. Crucially, and in contrast to previously proposed relaxations, the feasible solutions of our SDP all correspond to strongly stable'' policies that mix exponentially fast to a steady state. View details
    Preview abstract Data-independent methods for dimensionality reduction such as random projections, sketches, and feature hashing have become increasingly popular in recent years. These methods often seek to reduce dimensionality while preserving the hypothesis class, resulting in inherent lower bounds on the size of projected data. For example, preserving linear separability requires Ω(1/γ2 ) dimensions, where γ is the margin, and in the case of polynomial functions, the number of required dimensions has an exponential dependence on the polynomial degree. Despite these limitations, we show that the dimensionality can be reduced further while maintaining performance guarantees, using improper learning with a slightly larger hypothesis class. In particular, we show that any sparse polynomial function of a sparse binary vector can be computed from a compact sketch by a single-layer neural network, where the sketch size has a logarithmic dependence on the polynomial degree. A practical consequence is that networks trained on sketched data are compact, and therefore suitable for settings with memory and power constraints. We empirically show that our approach leads to networks with fewer parameters than related methods such as feature hashing, at equal or better performance. View details
    Preview abstract Entity resolution is the task of linking each mention of an entity in text to the corresponding record in a knowledge base (KB). Coherence models for entity resolution encourage all referring expressions in a document to resolve to entities that are related in the KB. We explore attention-like mechanisms for coherence, where the evidence for each candidate is based on a small set of strong relations, rather than relations to all other entities in the document. The rationale is that document-wide support may simply not exist for non-salient entities, or entities not densely connected in the KB. Our proposed system outperforms state-of-the-art systems on the CoNLL 2003, TAC KBP 2010, 2011 and 2012 tasks. View details
    Preview abstract We propose a new approach to the task of fine grained entity type classifications based on label embeddings that allows for information sharing among related labels. Specifically, we learn an embedding for each label and each feature such that labels which frequently co-occur are close in the embedded space. We show that it outperforms state-of-the-art methods on two fine grained entity-classification benchmarks and that the model can exploit the finer-grained labels to improve classification of standard coarse types. View details
    Plato: A Selective Context Model for Entity Resolution
    Michael Ringgaard
    Transactions of the Association for Computational Linguistics, vol. 3 (2015), pp. 503-515
    Preview abstract We present Plato, a probabilistic model for entity resolution that includes a novel approach for handling noisy or uninformative features,and supplements labeled training data derived from Wikipedia with a very large unlabeled text corpus. Training and inference in the proposed model can easily be distributed across many servers, allowing it to scale to over 10^7 entities. We evaluate Plato on three standard datasets for entity resolution. Our approach achieves the best results to-date on TAC KBP 2011 and is highly competitive on both the CoNLL 2003 and TAC KBP 2012 datasets. View details
    Preview abstract Entity type tagging is the task of assigning category labels to each mention of an entity in a document. While standard systems focus on a small set of types, recent work (Ling and Weld, 2012) suggests that using a large fine-grained label set can lead to dramatic improvements in downstream tasks. In the absence of labeled training data, existing fine-grained tagging systems obtain examples automatically, using resolved entities and their types extracted from a knowledge base. However, since the appropriate type often depends on context (e.g. Washington could be tagged either as city or government), this procedure can result in spurious labels, leading to poorer generalization. We propose the task of context-dependent fine type tagging, where the set of acceptable labels for a mention is restricted to only those deducible from the local context (e.g. sentence or document). We introduce new resources for this task: 11,304 mentions annotated with their context-dependent fine types, and we provide baseline experimental results on this data. View details
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